AMEA-YOLO: a lightweight remote sensing vehicle detection algorithm based on attention mechanism and efficient architecture

被引:0
作者
Shou-Bin Wang
Zi-Meng Gao
Deng-Hui Jin
Shu-Ming Gong
Gui-Li Peng
Zi-Jian Yang
机构
[1] Tianjin Chengjian University,School of Control and Mechanical
[2] STECOL Corporation,undefined
[3] Power Construction Corporation of China,undefined
来源
The Journal of Supercomputing | 2024年 / 80卷
关键词
Remote sensing images; Vehicle inspection; Lightweight network; High resolution; YOLO;
D O I
暂无
中图分类号
学科分类号
摘要
Due to the large computational requirements of object detection algorithms, high-resolution remote sensing vehicle detection always involves numerous small objects, high level of background complexity, and challenges in balancing model accuracy and parameter count. The attention mechanism and efficient architecture lightweight-YOLO (AMEA-YOLO) is proposed in this paper. A lightweight network as the backbone network of AMEA-YOLO is designed, and it could maintain model accuracy and ensure good lightweight. FasterNet is employed to accelerate model training speed. The enhanced deep second-order channel attention module (EnhancedSOCA) is utilized to improve the image high-resolution. In addition, a lightweight module is devised to further reduce the model’s weight. The implementation of the HardSwish activation function improves model accuracy. The experimental results indicate that the AMEA-YOLO algorithm could ensure model lightweight and accurate performance.
引用
收藏
页码:11241 / 11260
页数:19
相关论文
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[1]  
Peng G(2023)AMFLW-YOLO: a lightweight network for remote sensing image detection based on attention mechanism and multiscale feature fusion IEEE Trans Geosci Remote Sens 61 1-16
[2]  
Yang Z(2022)Automatic abdominal hernia mesh detection based on YOLOM IEEE Access 10 31420-31431
[3]  
Wang S(2021)YOLOP: you only look once for panoptic driving perception Mach Intell Res 19 550-562
[4]  
Zhou Y(2023)Automatic recognition of defects behind railway tunnel linings in GPR images using transfer learning Measurement 224 1671-1683
[5]  
Chen S(2022)Detection of maize tassels for UAV remote sensing image with an improved YOLOX model J Integr Agric 22 10117-10138
[6]  
Xu J(2023)LODNU: lightweight object detection network in UAV vision J Supercomput 79 117-18224
[7]  
Yu J(2023)Special vehicle detection from UAV perspective via YOLO-GNS based deep learning network Drones 7 18209-1116
[8]  
Wu J(2022)A lightweight network for vehicle detection based on embedded system J Supercomput 78 1109-1415
[9]  
Zhou G(2022)Real-time detection of flame and smoke using an improved YOLOv4 network SIViP 16 4930-8094
[10]  
Wu D(2022)Fire-YOLO: a small target object detection method for fire inspection Sustainability 14 1390-4462